On the Choice of Test Statistic for Conditional Moment Inequalities

Timothy Armstrong

No 1960, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper derives asymptotic power functions for Cramer-von Mises (CvM) style tests for conditional moment inequality models in the set identified case. Combined with power results for Kolmogorov-Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for tests based on kernel estimates, kernel bandwidth. The results show that KS tests are preferred to CvM tests, and that a truncated variance weighting is preferred to bounded weightings under a minimax criterion, and for a class of alternatives that arises naturally in these models. The results also provide insight into how moment selection and the choice of instruments affect power. Such considerations have a large effect on power for instrument based approaches when a CvM statistic or an unweighted KS statistic is used and relatively little effect on power with optimally weighted KS tests.

Keywords: Moment inequalities; Relative efficiency (search for similar items in EconPapers)
JEL-codes: C10 C12 C14 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2014-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: On the choice of test statistic for conditional moment inequalities (2018)
Working Paper: On the Choice of Test Statistic for Conditional Moment Inequalities (2017)
Working Paper: On the Choice of Test Statistic for Conditional Moment Inequalities (2016)
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